nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒07‒17
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting By Fulvio Corsi; Davide Pirino; Roberto Reno'
  2. HEGY Tests in the Presence of Moving Averages By Tomás del Barrio Castro; Denise R. Osborn
  3. A Computationally Practical Simulation Estimation Algorithm for Dynamic Panel Data Models with Unobserved Endogenous State Variables By Michael P. Keane; Robert M. Sauer
  4. Common factors in nonstationary panel data with a deterministic trend - estimation and distribution theory By Katarzyna Maciejowska
  5. Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis By Katarzyna Maciejowska
  6. QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles By Nyberg, Henri
  7. Noncausal Vector Autoregression By Lanne, Markku; Saikkonen, Pentti
  8. Optimal Forecasting of Noncausal Autoregressive Time Series By Lanne, Markku; Luoto, Jani; Saikkonen, Pentti
  9. GMM Estimation with Noncausal Instruments By Lanne, Markku; Saikkonen, Pentti
  10. Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models By Lanne, Markku; Luoma, Arto; Luoto, Jani

  1. By: Fulvio Corsi; Davide Pirino; Roberto Reno'
    Abstract: This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not only consistent, but also scarcely plagued by small-sample bias. To this purpose, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower vari- ation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic varia- tion in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.
    Keywords: volatility estimation, jump detection, volatility forecasting, threshold estimation, financial markets
    JEL: G1 C1 C22 C53
    Date: 2010–07–06
  2. By: Tomás del Barrio Castro (Universitat de les Illes Balears); Denise R. Osborn (University of Manchester)
    Abstract: We analyze the asymptotic distributions associated with the seasonal unit root tests of the Hylleberg et al. (1990) procedure for quarterly data when the innovations follow a moving average process. Although both the t- and F-type tests suffer from scale and shift effects compared with the presumed null distributions when a fixed order of autoregressive augmentation is applied, these effects disappear when the order of augmentation is sufficiently large. However, as found by Burridge and Taylor (2001) for the autoregressive case, individual t-ratio tests at the the semi-annual frequency are not pivotal even with high orders of augmentation, although the corresponding joint F-type statistic is pivotal. Monte Carlo simulations verify the importance of the order of augmentation for finite samples generated by seasonally integrated moving average processes.
    Keywords: Seasonal integration, HEGY tests, unit root tests, moving averages
    JEL: C12 C22
    Date: 2010
  3. By: Michael P. Keane (University of Technology Sydney and Arizona State University); Robert M. Sauer (University of Bristol)
    Abstract: This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed “initial conditions problem,” as well as the more general problem of missing state variables during the sample period. Repeated sampling experiments on dynamic probit models with serially correlated errors indicate that the estimator has good small sample properties. We apply the estimator to a model of married women’s labor force participation decisions. The results show that the rarely used Polya model, which is very difficult to estimate given missing data problems, fits the data substantially better than the popular Markov model. The Polya model implies far less state dependence in employment status than the Markov model. It also implies that observed heterogeneity in education, young children and husband income are much more important determinants of participation, while race is much less important.
    Keywords: Initial Conditions, Missing Data, Simulation, Female Labor Force Participation Decisions
    JEL: C15 C23 C25 J13 J21
    Date: 2010–07–05
  4. By: Katarzyna Maciejowska
    Abstract: The paper studies large-dimention factor models with nonstationary factors and allows for deterministic trends and factors integrated of order higher then one.We follow the model speci.cation of Bai (2004) and derive the convergence rates and the limiting distributions of estimated factors, factors loadings and common components. We discuss in detail a model with a linear time trend. We ilustrate the theory with an empirical exmple that studies the fluctuations of the real activity of U.S.economy. We show that these .uctuationas can be explained by two nonstationary factors and a small number of stationary factors. We test the economic interpretation of nonstationary factors.
    Keywords: Common-stochastic trends; Dynamic factors; Generalized dynamic factor models; Principal components; Nonstationary panel data
    JEL: C13 C33 C43
    Date: 2010
  5. By: Katarzyna Maciejowska
    Abstract: This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structural VAR models with a mixture of distributions. Hence the problem does not have a closed form solution, numerical optimization procedures need to be used. A Monte Carlo experiment is design to compare the performance of four maximization algorithms and two estimation strategies. It is shown that the EM algorithm outperforms the general maximization algorithms such as BFGS, NEWTON and BHHH. Moreover simplification of the probelm introduced in the two steps quasi ML method does not worsen small sample properties of the estimators and therefore may be recommended in the empirical analysis.
    Keywords: Structural vetcor autoregression , Error correction models, Mixed normal, Monte Carlo
    JEL: C32 C46
    Date: 2010
  6. By: Nyberg, Henri
    Abstract: In the empirical finance literature findings on the risk return tradeoff in excess stock market returns are ambiguous. In this study, we develop a new QR-GARCH-M model combining a probit model for a binary business cycle indicator and a regime switching GARCH-in-mean model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional ICAPM, there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.
    Keywords: Regime switching GARCH model; GARCH-in-mean model; probit model; stock return; risk-return tradeoff; business cycle
    JEL: C32 E32 G12 E44
    Date: 2010–04
  7. By: Lanne, Markku; Saikkonen, Pentti
    Abstract: In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications which currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.
    Keywords: Vector autoregression; noncausal time series; non-Gaussian time series
    JEL: C32 E43 C52
    Date: 2010–04
  8. By: Lanne, Markku; Luoto, Jani; Saikkonen, Pentti
    Abstract: In this paper, we propose a simulation-based method for computing point and density forecasts for univariate noncausal and non-Gaussian autoregressive processes. Numerical methods are needed to forecast such time series because the prediction problem is generally nonlinear and no analytic solution is therefore available. According to a limited simulation experiment, the use of a correct noncausal model can lead to substantial gains in forecast accuracy over the corresponding causal model. An empirical application to U.S. inflation demonstrates the importance of allowing for noncausality in improving point and density forecasts.
    Keywords: Noncausal autoregression; density forecast; inflation
    JEL: C53 C63 E31 C22
    Date: 2010–02
  9. By: Lanne, Markku; Saikkonen, Pentti
    Abstract: Lagged variables are often used as instruments when the generalized method of moments (GMM) is applied to time series data. We show that if these variables follow noncausal autoregressive processes, their lags are not valid instruments and the GMM estimator is inconsistent. Moreover, in this case, endogeneity of the instruments may not be revealed by the J-test of overidentifying restrictions that may be inconsistent and, as shown by simulations, its finite-sample power is, in general, low. Although our explicit results pertain to a simple linear regression, they can be easily generalized. Our empirical results indicate that noncausality is quite common among economic variables, making these problems highly relevant.
    Keywords: Noncausal autoregression; instrumental variables; test of overidentifying restrictions
    JEL: C51 C12 C22
    Date: 2009–09
  10. By: Lanne, Markku; Luoma, Arto; Luoto, Jani
    Abstract: In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the posterior model probability provides a convenient model selection criterion and yields information on the probabilities of the alternative causal and noncausal specifications. This is particularly useful in assessing economic theories that imply either causal or purely noncausal dynamics. As an empirical application, we consider U.S. inflation dynamics. A purely noncausal AR model gets the strongest support, but there is also substantial evidence in favor of other noncausal AR models allowing for dependence on past inflation. Thus, although U.S. inflation dynamics seem to be dominated by expectations, the backward-looking component is not completely missing. Finally, the noncausal specifications seem to yield inflation forecasts which are superior to those from alternative models especially at longer forecast horizons.
    Keywords: Noncausality; Autoregression; Bayesian model selection; Forecasting
    JEL: C52 E31 C22 C11
    Date: 2009–09

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